AFFIXCON Audience Data Pool Definition

Sub-audiences live organically inside the primary audience and can be profiled as follows  .

AFFIXCON segments people and households based on demographics, shopping habits, online interests and lifestyles, drivers of purchase, brand engagement, physical visitation, mobile app categories, everyday banking, general spending, major products held, homeowners and renters. The main audience is created from filters selected from the aforementioned data map while sub-audiences organically live inside the main audience and are available for profiling also.

In the example below, macro (e.g., gym visitors) or granular segments (e.g., Nike visitors) are able to be created within the sports retail category using our identifiers.


Variables include gender, age, marital status, household income, and children at home.

Drivers of purchase

Online and offline consumers are modeled and segmented from a battery of rational and emotional drivers of purchase-including brand affinity, reputation, and price/value. This information can inform one on how to best allocate their time and resources (e.g., investment levels, content and message personalization creation) towards creating new customer value propositions that are geared around the purchase drivers that matter most to consumers. For example, if for online shoppers the key purchase drivers were recommendations, brands could boost their profile and credentials on comparison websites, social media, or ratings and review sites.

Online interests and lifestyles

Segments are created from consumer search behaviors online using data-driven processes (advanced keyword research) including product discovery, discussion and shares on forums and blogs, product selection and purchasing. For example, consumers who search about Zoom will be segmented as web conferencing for Zoom.

Shopping habits

Variables include online and offline behaviors focusing on specific goods and services relevant to the audience of interest. For example, gamers and parents of school children.

Mobile app categories 

Segments are created from app categories of interest installed on iOS and android mobile devices. This data enables one to access a fuller picture of online behaviors across sites and apps.

Brand engagement 

Variables include brand search, visitation, and transactional behavior. Brand engagement is correlated with the appeal, satisfaction, loyalty, and advocacy. Insights into brand engagement can assist with allocating sufficient resources towards attacking or defending against particular brands.

Location data with geofencing capability / Physical visitation

Segments are created principally from mobile native location detection algorithms. Physical visitation by categories related to the audience is available for channels including stores and showrooms. For example, for automotive brands, new car showroom visitors are captured.

Everyday banking

Segments are created from banking app data and mobile device native location detection. Insight into financial personalities gives a view of the relative mix of traditionalists and those who prefer newer and more boutique options outside of the mainstream. One can use this data to also identify potentially fruitful opportunities for partnering with financial institutions by way of cross-promotion offers.

General spending

General spending segments are diverse and span most key consumer verticals. Macro-level shopping activity data can help one understand market dynamics and inform how to best prioritize advertising efforts around products and services which are most engaged with, trending, and not trending. For example, women’s t-shirts are a general spend segment; brand or shop information is not captured.

Major products held

Products held include insurance and automotive products and services. This information can help one understand product and lifestyle preferences.

Homeowners and renters

Variables include dwelling type, size, key features, occupancy duration, last advertised rental, and sale price. This information can help one identify properties with certain features. For example, a cleaning company can target houses with 3+ bedrooms.

AFFIXCON has developed proven techniques to retrieve results on the above-mentioned audience insight identification concepts. We are looking forward to collaborating with you as channel partners to share our techniques with the community. Are you interested in learning more about how we can more efficiently identify your client’s audience insights?

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